

** CODE for replication of Can conservatism make women more vulnerable to violence?
** Authors:  Victor Araujo Malu Gatto
** Journal: Comparative Political Studies (CPS)

** Before running this code, please see the "READ_ME_FIRST" file for general instructions. 
** All procedures and analysis were executed using a license of STATA 16 


******************************************
*** Data for individual-level analyses ***
******************************************

*** Importing the data from repository to STATA
clear
import delimited "G:\My Drive\Araújo_Gatto_papers\mulheres_MUNIC\Policies to tackle Violence against Women\data\CPS_materials\data_datafolha.csv"
** Source: DataFolha (2019)

*** Installing required packages
ssc install estout, replace
ssc install blindschemes, replace
ssc install coefplot, replace


***************************************************************
*** Creating dependent variables (DV) used in main analyses ***
***************************************************************
 
 
** In the last year, violence against women in Brazil increased
* Version in portuguese (A violência contra a mulher  aumentou no Brasil no último ano)
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)"

gen viol_woman = p51a
replace viol_woman = 0 if p51a ==2
replace viol_woman = 0 if p51a ==3
replace viol_woman = 0 if p51a ==4
replace viol_woman = 0 if p51a ==5


** Existing laws in Brazil are adequate to protect women
* Original question in portuguese: As leis no Brasil são adequadas para proteger as mulheres
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)"

gen law_woman = p51b
replace law_woman = 0 if p51b ==2
replace law_woman = 0 if p51b ==3
replace law_woman = 0 if p51b ==4
replace law_woman = 0 if p51b ==5

** Media exaggerates in their coverage of cases of violence against women
* Original question in portuguese: A imprensa exagera na exposição dos casos de violência contra a mulher
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)"

gen press_woman = p51c
replace press_woman = 0 if p51c ==2
replace press_woman = 0 if p51c ==3
replace press_woman = 0 if p51c ==4
replace press_woman = 0 if p51c ==5




*****************************************************************
*** Creating independent variables (IV) used in main analyses ***
*****************************************************************

* Reject feminism (variable assumes 1 for those respondents that are not feminist nor a supporter of feminism)
* Original questions in portuguese:  1. Você se considera feminista?; 2. Você apoia o feminismo?
gen reject_fem =.
replace reject_fem = 0 if p48 == 1 | p48a == 1
replace reject_fem = 1 if reject_fem ==.


* Are you for or against Brazil receiving refugees from Venezuela?
* Original question in portuguese: Você é a favor ou contra que o Brasil receba refugiados da Venezuela?
* Variable assumes 1 when respondent says "AGAINST" (CONTRA)

gen rejec_refug = p47
replace rejec_refug = 1 if p47 ==2
replace rejec_refug = 0 if p47 ==1
replace rejec_refug = 0 if p47 ==3
replace rejec_refug = 0 if p47 ==.


* The more people that are incarcerated, the more safe is society
* Original question in portuguese: Quanto mais pessoas presas, mais segura estará a sociedade
* Variable assumes 1 when respondent says "strongly agree (concorda totalmente)"

gen prisao_pref = p42e
replace prisao_pref = 0 if p42e ==2
replace prisao_pref = 0 if p42e ==3
replace prisao_pref = 0 if p42e ==4
replace prisao_pref = 0 if p42e ==5


** Cumulative Index of conservatism
** To derivate this index, we calculate the sum of individuals' answers to all three variables above 
gen cumu_conserv = rejec_refug + reject_fem + prisao_pref


** Conservatism measure (binary)
* Variable assumes 1 when respondent is conservative in at least one of the dimensions presented above
gen conserv_bina =.
replace conserv_bina = 1 if rejec_refug == 1 | reject_fem == 1 |  prisao_pref == 1
replace conserv_bina = 0 if conserv_bina ==.




********************************************************************
*** Creating alternative dependent variables (Robustness checks) ***
********************************************************************

** In this case, we create less restrictive dummy categorizations for dependent variables

** Group 2

** In the last year, violence against women in Brazil increased
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)" or "partially agree (concorda parcialmente)"

gen viol_woman2 = p51a
replace viol_woman2 = 1 if p51a ==2
replace viol_woman2 = 0 if p51a ==3
replace viol_woman2 = 0 if p51a ==4
replace viol_woman2 = 0 if p51a ==5


** Existing laws in Brazil are adequate to protect women
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)" or "partially agree (concorda parcialmente)"

gen law_woman2 = p51b
replace law_woman2 = 1 if p51b ==2
replace law_woman2 = 0 if p51b ==3
replace law_woman2 = 0 if p51b ==4
replace law_woman2 = 0 if p51b ==5


** Media exaggerates in their coverage of cases of violence against women
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)" or "partially agree (concorda parcialmente)"

gen press_woman2 = p51c
replace press_woman2 = 1 if p51c ==2
replace press_woman2 = 0 if p51c ==3
replace press_woman2 = 0 if p51c ==4
replace press_woman2 = 0 if p51c ==5



** Group 3

** In the last year, violence against women in Brazil increased
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)", "partially agree (concorda parcialmente)", or "neither agree nor disagree (nem concorda nem discorda)"

gen viol_woman3 = p51a
replace viol_woman3 = 1 if p51a ==2
replace viol_woman3 = 1 if p51a ==3
replace viol_woman3 = 0 if p51a ==4
replace viol_woman3 = 0 if p51a ==5


** Existing laws in Brazil are adequate to protect women
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)", "partially agree (concorda parcialmente)", or "neither agree nor disagree (nem concorda nem discorda)"
gen law_woman3 = p51b
replace law_woman3 = 1 if p51b ==2
replace law_woman3 = 1 if p51b ==3
replace law_woman3 = 0 if p51b ==4
replace law_woman3 = 0 if p51b ==5


** Media exaggerates in their coverage of cases of violence against women
* Variable assumes 1 if respondent "strongly agree (concorda totalmente)", "partially agree (concorda parcialmente)", or "neither agree nor disagree (nem concorda nem discorda)"

gen press_woman3 = p51c
replace press_woman3 = 1 if p51c ==2
replace press_woman3 = 1 if p51c ==3
replace press_woman3 = 0 if p51c ==4
replace press_woman3 = 0 if p51c ==5




************************************************************
*** Creating control variables included in main analyses ***
************************************************************

** Municipality size (categorical variable)
gen size_munic = porte
** Age
gen age = idade1
**Gender
gen woman = mulher
**Schooling
gen educ = escola
**Income
gen income = rendaf 
**Race
gen black = cor
replace black = 1 if cor == 2
replace black = 0 if cor == 1
replace black = 0 if cor == 3
replace black = 0 if cor == 4
replace black = 0 if cor == 5
replace black = 0 if cor == .
** Evangelical
gen evang = religiao
replace evang = 1 if religiao == 3
replace evang = 1 if religiao == 1
replace evang = 1 if religiao == 2
replace evang = 1 if religiao == 4
replace evang = 1 if religiao == 5
replace evang = 0 if religiao == 6
replace evang = 0 if religiao == 7
replace evang = 0 if religiao == 8
replace evang = 0 if religiao == 9
replace evang = 0 if religiao == 10
replace evang = 0 if religiao == 11
replace evang = 0 if religiao == 12
** PT voters (respondents that identify themselves as PT supporters)
gen pt_voters = partido
*PT
replace pt_voters = 1 if partido ==2
*PCdoB
replace pt_voters = 0 if partido ==20
*PSOL
replace pt_voters = 0 if partido ==10
*PSB
replace pt_voters = 0 if partido ==8
*PDT
replace pt_voters = 0 if partido ==7
*PP
replace pt_voters = 0 if partido ==3
*PSDB
replace pt_voters = 0 if partido ==4
*PTB
replace pt_voters = 0 if partido ==5
*DEM
replace pt_voters = 0 if partido ==9
*PV
replace pt_voters = 0 if partido ==14
*PSD
replace pt_voters = 0 if partido ==15
*PR
replace pt_voters = 0 if partido ==16
*PATRIOTAS
replace pt_voters = 0 if partido ==17
*PSL
replace pt_voters = 0 if partido ==18
*REDE
replace pt_voters = 0 if partido ==19
*PRB
replace pt_voters = 0 if partido ==21
*PPS
replace pt_voters = 0 if partido ==22
*PODEMOS
replace pt_voters = 0 if partido ==23
* Other
replace pt_voters = 0 if partido ==.



*************************************
*** Conducting Empirical analyses ***
*************************************

** creating a respondent id for the clusterization in the logit regression models  
egen number_id = seq()

** Here we test the effect of conservatism on attitudes towards Violence Againts Woman (VAW)

* Our measure of conservatism is based on individuals' preferences in the following issues:
    * Agreement of mass incarceration (it assumes 1 if individual agrees);
	* Rejection of feminism (it assumes 1 if individual rejects);
	* Rejection of refugees coming to Brazil from Venezuela (it assumes 1 if individual rejects)
* For more details on how we classify each one of these variables, please see command lines between 20-176
	
	
** We then developed two measures of conservatism: 
*(A): a counting variable that varies between 0 and 3; where 0 means the absence of conservatism and 3 means the maximum level of conservatism in our sample of respondents (cumu_conserv)
*(B): a binary variable that assumes 1 if individual scores 1 in at least one of our measures of conservatism (conserv_bina)


**********************************************************************************
*** Analyses with measure A of conservatism (cumu_conserv) - TABLE 3 MAIN TEXT ***
**********************************************************************************

eststo clear
*(i) DV: there are enough policies to prevent violence against women
eststo: logit law_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(ii) DV: the violence against women is increasing 
eststo: logit viol_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: logit press_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(cumu_conserv "Conservatism (index)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (index) on attitudes towards VAW\label{indiv_index})
eststo clear



**************************************************************************************************************
*** Plotting the predicted probability using Measure A of conservatism (cumu_conserv) - FIGURE 3 MAIN TEXT ***
**************************************************************************************************************

set scheme plotplain

logit law_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)   
margins, at(cumu_conserv=(0(1)3)) post
estimates store m_1
coefplot m_1, title("(A) Laws in Brazil are adequate to protect women") ytitle(Predicted probability of agreement) xtitle(Conservatism (index)) ///
    at recast(line) lwidth(*2) ciopts(recast(rline) lpattern(dash)) levels(95)	
	 

logit viol_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)   
margins, at(cumu_conserv=(0(1)3)) post
estimates store m_2 
coefplot m_2, title("(B) In the last year, VAW in Brazil increased") ytitle() xtitle(Conservatism (index)) ///
    at recast(line) lwidth(*2) ciopts(recast(rline) lpattern(dash)) levels(95)	
	

logit press_woman cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)  
margins, at(cumu_conserv=(0(1)3)) post
estimates store m_3
coefplot m_3, title("(C) Media exaggerates in their coverage of cases of VAW") ytitle() xtitle(Conservatism (index)) ///
    at recast(line) lwidth(*2) ciopts(recast(rline) lpattern(dash)) levels(95)	
	 
	 
	
** Histogram of electoral conservatism
set scheme plotplain
histogram cumu_conserv, discrete gap(10) percent


** Combining the figures created above (do not forget to update your directory before running the lines below)
** The figures created above must be in the chosen directory	
	
cd "G:\My Drive\Araújo_Gatto_papers\mulheres_MUNIC\Policies to tackle Violence against Women\data\CPS_figures"
		
graph combine figure1a.gph figure2a.gph figure3a.gph, ///
name("firstset", replace) ycommon cols(3) title("")
graph combine hist_conservatism2.gph, ///
name("secondset", replace) ycommon cols(1) title("")
graph combine firstset secondset, ///
saving("sevenpanelgraph.gph", replace) ycommon cols(1) 
*graph export sevenpanelgraph.eps, replace	


************************************************
** Descriptive Statistics - TABLE 4 APPENDIX ***
************************************************

** Dependent variables
su viol_woman law_woman press_woman 
** DV: Alternative classifications
su viol_woman2 law_woman2 press_woman2 viol_woman3 law_woman3 press_woman3
** Measures of conservatism
su cumu_conserv conserv_bina reject_fem rejec_refug prisao_pref 
** Controls
su age woman educ income black evang pt_voters size_munic



************************************************************************************************
*** Baseline (without controls): Measure A of conservatism (cumu_conserv) - TABLE 5 APPENDIX ***
************************************************************************************************

*(i) DV: there are enough policies to prevent violence against women (without control)
eststo: logit law_woman cumu_conserv, cluster (number_id) 
*(iv) DV: the violence against women is increasing (with control)
eststo: logit viol_woman cumu_conserv, cluster (number_id) 
*(v) DV: the media coverage overestimate the cases of violence against women (without control)
eststo: logit press_woman cumu_conserv, cluster (number_id) 
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(cumu_conserv "Conservatism (index)") ///
title(The effect of conservatism (dummy) on attitudes towards VAW\label{indiv_bina_base})
eststo clear 



************************************************************************************************
*** Measure B of conservatism (conserv_bina), Baseline (without controls) - TABLE 6 APPENDIX ***
************************************************************************************************

*(i) DV: there are enough policies to prevent violence against women (without control)
eststo: logit law_woman conserv_bina, cluster (number_id) 
*(ii) DV: the violence against women is increasing (without control)
eststo: logit viol_woman conserv_bina, cluster (number_id) 
*(iii) DV: the media coverage overestimate the cases of violence against women (without control)
eststo: logit press_woman conserv_bina, cluster (number_id)  
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(conserv_bina "Conservatism (dummy)") ///
title(The effect of conservatism (dummy) on attitudes towards VAW\label{indiv_bina_base})
eststo clear 



***************************************************************************************************
*** GROUP 2 of dependent variables, Measure A of conservatism (cumu_conserv) - TABLE 7 APPENDIX ***
***************************************************************************************************

* Here we make the classification of our dependent variables less restrictive. We group the categories "Totally agree" and "Partially agree" instead of only take into account "Totally agree" in the category of reference (1) 

*(i) DV: there are enough policies to prevent violence against women 
eststo: logit law_woman2 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)     
*(ii) DV: the violence against women is increasing (with control)
eststo: logit viol_woman2 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)    
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: logit press_woman2 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)  
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(cumu_conserv "Conservatism (index)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (index) on attitudes towards VAW - alternative classification of DVs\label{index_altern})
eststo clear



***************************************************************************************************
*** GROUP 2 of dependent variables, Measure B of conservatism (conserv_bina) - TABLE 8 APPENDIX ***
***************************************************************************************************

 * Here we make the classification of our dependent variables less restrictive. We group the categories "Totally agree" and "Partially agree" instead of only take into account "Totally agree" in the category of reference (1) 
 
*(i) DV: there are enough policies to prevent violence against women 
eststo: logit law_woman2 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(ii) DV: the violence against women is increasing 
eststo: logit viol_woman2 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id)   
*(iii) DV: the media coverage overestimate the cases of violence against women
eststo: logit press_woman2 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id) 
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(conserv_bina "Conservatism (dummy)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (dummy) on attitudes towards VAW\label{alter_dummy1})
eststo clear



***************************************************************************************************
*** GROUP 3 of dependent variables, Measure A of conservatism (cumu_conserv) - TABLE 9 APPENDIX ***
***************************************************************************************************

* Here we make the classification of our dependent variables less restrictive. We group the categories "Totally agree", "Partially agree", and  "Neither agree nor disagree" instead of only take into account "Totally agree" in the category of reference (1) 

*(i) DV: there are enough policies to prevent violence against women 
eststo: logit law_woman3 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)   
*(ii) DV: the violence against women is increasing 
eststo: logit viol_woman3 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(iii) DV: the media coverage overestimate the cases of violence against women
eststo: logit press_woman3 cumu_conserv woman black age educ income evang pt_voters size_munic, cluster (number_id) 
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(cumu_conserv "Conservatism (index)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (index) on attitudes towards VAW - alternative classification of DVs\label{alter_index2})
eststo clear



****************************************************************************************************
*** GROUP 3 of dependent variables, Measure B of conservatism (conserv_bina) - TABLE 10 APPENDIX ***
****************************************************************************************************

* Here we make the classification of our dependent variables less restrictive. We group the categories "Totally agree", "Partially agree", and "Neither agree nor disagree" instead of only take into account "Totally agree" in the category of reference (1) 
 
eststo clear 
*(i) DV: there are enough policies to prevent violence against women 
eststo: logit law_woman3 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(ii) DV: the violence against women is increasing 
eststo: logit viol_woman3 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id)  
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: logit press_woman3 conserv_bina woman black age educ income evang pt_voters size_munic, cluster (number_id) 
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(conserv_bina "Conservatism (dummy)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (dummy) on attitudes towards VAW - alternative classification of DVs\label{alter_dummy2})
eststo clear



********************************************************************
*** Measure B of conservatism (conserv_bina) - TABLE 11 APPENDIX ***
********************************************************************

*(i) DV: there are enough policies to prevent violence against women 
eststo: logit law_woman conserv_bina woman black age educ income evang pt_voters size_munic,  cluster (number_id)   
*(ii) DV: the violence against women is increasing 
eststo: logit viol_woman conserv_bina woman black age educ income evang pt_voters size_munic,  cluster (number_id)   
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: logit press_woman conserv_bina woman black age educ income evang pt_voters size_munic,  cluster (number_id)  
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(conserv_bina "Conservatism (dummy)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (dummy) on attitudes towards VAW\label{indiv_bina})
eststo clear


******************************************************************************************************
*** Measure A of conservatism (cumu_conserv), Municipality-level fixed effects - TABLE 12 APPENDIX ***
******************************************************************************************************

xtset cidade
*(i) DV: there are enough policies to prevent violence against women 
eststo: xtlogit  law_woman cumu_conserv woman black age educ income evang pt_voters, fe  
*(ii) DV: the violence against women is increasing 
eststo: xtlogit  viol_woman cumu_conserv woman black age educ income evang pt_voters, fe 
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: xtlogit  press_woman cumu_conserv woman black age educ income evang pt_voters, fe
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(cumu_conserv "Conservatism (index)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (index) on attitudes towards VAW - with municipal-level fixed effects\label{index_fe})
eststo clear


******************************************************************************************************
*** Measure B of conservatism (conserv_bina), Municipality-level fixed effects - TABLE 13 APPENDIX ***
******************************************************************************************************

*(i) DV: there are enough policies to prevent violence against women
eststo: xtlogit  law_woman conserv_bina woman black age educ income evang pt_voters, fe 
*(ii) DV: the violence against women is increasing 
eststo: xtlogit  viol_woman conserv_bina woman black age educ income evang pt_voters, fe 
*(iii) DV: the media coverage overestimate the cases of violence against women 
eststo: xtlogit  press_woman conserv_bina woman black age educ income evang pt_voters, fe
esttab using example.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
stats(r2_p chi2 N N_clust, fmt(%9.3f %9.0g) labels("Pseudo R^{2}" "Wald chi2" "Observations" "N.Clusters")) ///
collabels(none) varlabels(conserv_bina "Conservatism (dummy)" woman "Woman" black "Black" age "Age" educ "Schooling" ///
income "Income" evang "Evangelical" pt_voters "PT voter" size_munic "Municipality size"_cons "Constant") ///
title(The effect of conservatism (dummy) on attitudes towards VAW - with municipal-level fixed effects\label{dummy_fe})
eststo clear




